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Reducing branch target mispredictions by optimizing polymorphic object grouping.
Distinct from Optimizer Performance Optimizations: Candidates focus on AI optimizers or GraphQL, not low-level C++ style virtual function call overhead
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perf-ninja is a collection of educational resources and curricula focused on CPU architecture, memory hierarchies, SIMD programming, and low-level performance engineering. It provides instructional material and practical labs for identifying and fixing CPU bottlenecks, such as cache misses and branch mispredictions. The project differentiates itself through specialized training in hardware-level optimizations, including the use of compiler intrinsics for SIMD vectorization and the implementation of branchless predicate execution to eliminate pipeline stalls. It also covers advanced binary-lev
Increases execution speed and reduces branch target mispredictions by grouping polymorphic objects of the same derived type.